报告主题：Similarity-Informed Transfer Learning for Multivariate Functional Censored Quantile Regression

时间：2025年10月22日13:00-14:00

地点：1-218

报告人：尤进红

报告内容简介：

To address the challenge of utilizing patient data from other organ transplant centers (source cohorts) to improve survival time estimation and inference for a target center (target cohort) with limited samples and strict data-sharing privacy constraints, we propose the Similarity-Informed Transfer Learning (SITL) method. This approach estimates multivariate functional censored quantile regression by flexibly leveraging information from each source cohort based on its similarity to the target cohort. Furthermore, the method is adaptable to continuously updated real-time data. We establish the asymptotic properties of the estimators obtained using the SITL method, demonstrating improved convergence rates. Additionally, we develop an enhanced approach that combines the SITL method with a resampling technique to construct more accurate confidence intervals for functional coefficients, backed by theoretical guarantees. Extensive simulation studies and an application to kidney transplant data illustrate the significant advantages of the SITL method. Compared to methods that rely solely on the target cohort or indiscriminately pool data across source and target cohorts, the SITL method substantially improves both estimation and inference performance.

主讲人简介：

尤进红教授，加拿大女皇大学（University of Regina）统计学博士，美国北卡罗纳教堂山分校博士后，上海财经大学统计与管理学院tenured常任轨教授、博士生导师和统计与数据科学研究院院长，全国工业统计学教学研究会第九届理事会副会长，全国统计学交叉学科分会副会长。尤进红教授有十余年的北美学习、工作经历。长期从事计量经济学、数理统计以及生物统计的科学研究； 在面板数据、函数型数据数据分析及其在经济学， 金融学和生物医学方面的应用开展了许多有价值的研究工作；在国际和国内著名的统计学、经济学和数据科学杂志（包括Journal of the American Statistical Association，Annals of Applied Statistics, Journal of Econometric，Journal of Machine Learning Research等）上发表学术论文将近百十余篇，他引超过一千四百余次；主持和参与了多个国家自科基金项目；培养了三十余位的博士生和博士后。为国际著名统计和计量经济学杂志Annals of Statistics, Journal of the American Statistical Association , Biometrika和Journal of Econometric等的论文评审人。曾获上海市教委教学成果一等奖。